Code for Unsupervised Multi-Source Domain Adaptation for Regression [1] paper
Every experiment was made using CUDA Drivers 9.0 and a Linux machine. Non GPU users can turn it down by specifying the device to 'cpu'. To reproduce our environment, one can use the following line: conda env create -f environment.yml
Data for the Amazon experiments can be downloaded and processed by running create_amazon.py Code also includes DANN [2] and MDAN [3]. For [3], the code was largely inspired from their original implementation (https://github.com/KeiraZhao/MDAN).
[1] Richard, G., de Mathelin, A., Hébrail, G., Mougeot, M., & Vayatis, N. (2020). Unsupervised Multi-Source Domain Adaptation for Regression.
[2] Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., ... & Lempitsky, V. (2016). Domain-adversarial training of neural networks. The Journal of Machine Learning Research, 17(1), 2096-2030.
[3] Zhao, H., Zhang, S., Wu, G., Moura, J. M., Costeira, J. P., & Gordon, G. J. (2018). Adversarial multiple source domain adaptation. In Advances in neural information processing systems (pp. 8559-8570).